ArticleBioMed research international2014
Final Gleason score prediction using discriminant analysis and support vector machine based on preoperative multiparametric MR imaging of prostate cancer at 3T.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies.European urology oncology · 2026Article
- Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.PloS one · 2026Article
- Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology.Scientific reports · 2024Article
- Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images.Journal of medical signals and sensors · 2024Article
- Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022.Quantitative imaging in medicine and surgery · 2023Article
- Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer.Frontiers in oncology · 2022Article
- An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.Theranostics · 2020Article
- A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer.Diagnostics (Basel, Switzerland) · 2019Article
- PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.Scientific reports · 2018Article
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6 authors.
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Abstract
objectiveThis study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. MATERIALS AND
methodsThirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation.
resultsUsing a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.
conclusionSVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.
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